Text data, including short texts, constitute a major share of web content. The availability of this data to billions of users triggers frequent plagiarism attacks. Authorship Attribution (AA) seeks to identify the most probable author of a given text based on similarity to the writing style of potential authors. In this paper, we approach AA as a writing style profile generation process, where we group text instances for each author into a single profile. We use Twitter as the source for our short Modern Standard Arabic (MSA) texts. Numerous experiments with various training approaches, tools and features allowed us to settle on a text representation method that relies on text concatenation of Arabic tweets to form chunks, which are then duplicated to reach a precalculated length. These chunks are used to train machine learning models for our 45 author profiles. This allowed us to achieve accuracies up to 99%, which compares favorably with the best results reported in the literature.